Global-Entropy Driven Exploration with Distributed Models under Sparsity Constraints
نویسندگان
چکیده
منابع مشابه
Minimum Error Entropy Algorithms with Sparsity Penalty Constraints
Recently, sparse adaptive learning algorithms have been developed to exploit system sparsity as well as to mitigate various noise disturbances in many applications. In particular, in sparse channel estimation, the parameter vector with sparsity characteristic can be well estimated from noisy measurements through a sparse adaptive filter. In previous studies, most works use the mean square error...
متن کاملEstimation of Covariance Matrices under Sparsity Constraints
Discussion of “Minimax Estimation of Large Covariance Matrices under L1-Norm” by Tony Cai and Harrison Zhou. To appear in Statistica Sinica. Introduction. Estimation of covariance matrices in various norms is a critical issue that finds applications in a wide range of statistical problems, and especially in principal component analysis. It is well known that, without further assumptions, the em...
متن کاملCooperative Exploration under Communication Constraints
The cooperative exploration problem necessarily involves communication among agents,while the spatial separation inherent in this task places fundamental limits on the amountof data that can be transmitted. However, the impact of limited communication on theexploration process has not been fully characterized. Existing exploration algorithms do notrealistically model the tra...
متن کاملDictionary learning under global sparsity constraint
A new method is proposed in this paper to learn overcomplete dictionary from training data samples. Differing from the current methods that enforce similar sparsity constraint on each of the input samples, the proposed method attempts to impose global sparsity constraint on the entire data set. This enables the proposed method to fittingly assign the atoms of the dictionary to represent various...
متن کاملDeformable models with sparsity constraints for cardiac motion analysis
Deformable models integrate bottom-up information derived from image appearance cues and top-down priori knowledge of the shape. They have been widely used with success in medical image analysis. One limitation of traditional deformable models is that the information extracted from the image data may contain gross errors, which adversely affect the deformation accuracy. To alleviate this issue,...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Applied Sciences
سال: 2018
ISSN: 2076-3417
DOI: 10.3390/app8101722